"In his magisterial Great American City, Robert J. Sampson puts social scientific data behind an argument that we all feel and experience everyday: the neighborhood you live in has a big effect on your life and the city you live in. Not only does your neighborhood determine where your nearest hospital is, what kind of schools your children can attend, or how many police officers you might encounter (and how they respond to you), it affects how you feel, how you think about the world and your place in it. Like many sociologists before him, Sampson looks to Chicago to make his insightful interventions, based on extensive data collected across the city's diverse neighborhoods. This edition includes a new afterword by Sampson reflecting on changes in Chicago and the country that have occurred since the book was initially published. He notes the increase in gun violence, both among civilians and police killings of civilians, as well as steady or growing rates of segregation despite an increase in diversity. With these changes have come new research, much of it a continuation or elaboration of the work in Great American City. He updates readers on the status of the research initiative that serves as the basis of Great American City, the Project on Human Development in Chicago Neighborhoods (PHDCN), and summarizes how scholars have taken up his work. Many of these scholars have new tools at their disposal with the rise of big data; Sampson remarks on these changes in the field"--

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

An innovative new anthology exploring how science fiction can motivate new approaches to economics. From the libertarian economics of Ayn Rand to Aldous Huxley's consumerist dystopias, economics and science fiction have often orbited each other. In Economic Science Fictions, editor William Davies has deliberately merged the two worlds, asking how we might harness the power of the utopian imagination to revitalize economic thinking. Rooted in the sense that our current economic reality is no longer credible or viable, this collection treats our economy as a series of fictions and science fiction as a means of anticipating different economic futures. It asks how science fiction can motivate new approaches to economics and provides surprising new syntheses, merging social science with fiction, design with politics, scholarship with experimental forms. With an opening chapter from Ha-Joon Chang as well as theory, short stories, and reflections on design, this book from Goldsmiths Press challenges and changes the notion that economics and science fiction are worlds apart. The result is a wealth of fresh and unusual perspectives for anyone who believes the economy is too important to be left solely to economists. Contributors AUDINT, Khairani Barokka, Carina Brand, Ha-Joon Chang, Miriam Cherry, William Davies, Mark Fisher, Dan Gavshon-Brady and James Pockson, Owen Hatherley, Laura Horn, Tim Jackson, Mark Johnson, Bastien Kerspern, Nora O Murchú, Tobias Revell et al., Judy Thorne, Sherryl Vint, Joseph Walton, Brian Willems

Corporate accountability is never far from the front page, and as one of the world’s most elite business schools, Harvard Business School trains many of the future leaders of Fortune 500 companies. But how does HBS formally and informally ensure faculty and students embrace proper business standards? Relying on his first-hand experience as a Harvard Business School faculty member, Michel Anteby takes readers inside HBS in order to draw vivid parallels between the socialization of faculty and of students. In an era when many organizations are focused on principles of responsibility, Harvard Business School has long tried to promote better business standards. Anteby’s rich account reveals the surprising role of silence and ambiguity in HBS’s process of codifying morals and business values. As Anteby describes, at HBS specifics are often left unspoken; for example, teaching notes given to faculty provide much guidance on how to teach but are largely silent on what to teach. Manufacturing Morals demonstrates how faculty and students are exposed to a system that operates on open-ended directives that require significant decision-making on the part of those involved, with little overt guidance from the hierarchy. Anteby suggests that this model—which tolerates moral complexity—is perhaps one of the few that can adapt and endure over time. Manufacturing Morals is a perceptive must-read for anyone looking for insight into the moral decision-making of today’s business leaders and those influenced by and working for them.

"Innovation and entrepreneurship are ubiquitous today, both as fields of study and as starting points for conversations among experts in government and economic development. But while these areas on continue to attract public and private investments, many measurements of their resulting economic growth-including productivity growth and business dynamism-have remained modest. Why this difference? Because not all business sectors are the same, and the transformative gains of some industries have been offset by stagnation or contraction in others. Accordingly, a nuanced understanding of the economy requires a nuanced understanding of where innovation and entrepreneurship occur and where they matter. Answering these questions allows for strategic public investment and the infrastructure for economic growth.The Role of Innovation and Entrepreneurship in Economic Growth, the latest entry in the NBER conference series, seeks to codify these answers. The editors leverage industry studies to identify specific examples of productivity improvements enabled by innovation and entrepreneurship, including those from new production technologies, increased competition, new organizational forms, and other means. Taken together, the volume illuminates whether the contribution of innovation and entrepreneurship to economic growth is likely to be concentrated, be it selected sectors or more broadly"--

A calculation of the social returns to innovation /Benjamin F. Jones and Lawrence H. Summers --Innovation and human capital policy /John Van Reenen --Immigration policy levers for US innovation and start-ups /Sari Pekkala Kerr and William R. Kerr --Scientific grant funding /Pierre Azoulay and Danielle Li --Tax policy for innovation /Bronwyn H. Hall --Taxation and innovation: what do we know? /Ufuk Akcigit and Stefanie Stantcheva --Government incentives for entrepreneurship /Josh Lerner.

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.